Engineering
Railway
100%
Deep Learning Method
61%
Subnetwork
30%
Urban Traffic
30%
Feedforward
30%
Traffic Pattern
30%
Joints (Structural Components)
30%
Level Feature
30%
Autoencoder
30%
Urban Transportation
30%
Computervision
30%
Collected Data
30%
Traffic Data
30%
Road
30%
Experimental Result
30%
Surface Defect
30%
Deep Neural Network
30%
Rail Surface
30%
Convolutional Neural Network
30%
Major Component
30%
Detection Performance
16%
Rail Track
15%
Bounding Box
15%
Simplifies
15%
Discriminator
15%
Extracted Feature
15%
Derailments
15%
Detection Procedure
15%
Hybrid Model
13%
Learning Approach
12%
Level Image
7%
Cutting Edge
7%
Pixel Level
7%
Feature Extraction
7%
Enhanced Model
6%
Inspection Result
6%
Computer Science
Subnetwork
30%
Generative Approach
30%
Semisupervised Learning
30%
Deep Learning Method
30%
Object Detection
30%
Deep Neural Network
30%
Traffic Condition
30%
Autoencoder
30%
Convolutional Neural Network
30%
YOLO
15%
Mean Average Precision
15%
Potential Accident
15%
Discriminator
15%
Detection Accuracy
15%
Detection Performance
15%
Adversarial Machine Learning
15%
Detection Procedure
15%
Feedforward Network
15%
Benchmarking
15%
Learning Approach
15%
Feature Extraction
7%
Attention (Machine Learning)
7%
Collected Data
7%
Subgraphs
7%
Extracted Feature
7%
Experimental Result
7%
Learning Technology
7%
Postprocessing
7%
Traffic Pattern
7%
Deep Convolutional Neural Networks
7%
Automatic Detection
7%
Focused Attention
7%
Training Process
7%
Traffic Information
7%
Data Augmentation
7%
Quality Solution
7%
Detection Pipeline
7%
